Before we roll up our sleeves and get to work, it might be beneficial to introduce some basic database concepts and look at the history of computerized data storage and retrieval.
This excerpt is from Learning SQL. Updated for the latest database management systems, this introductory guide will get you up and running with SQL quickly. Whether you need to write database applications, perform administrative tasks, or generate reports, Learning SQL, Second Edition, will help you easily master all the SQL fundamentals. Each chapter presents a self-contained lesson on a key SQL concept or technique, with numerous illustrations, annotated examples, and exercises to let you practice the skills you learn.
A database is nothing more than a set of related information. A telephone book, for example, is a database of the names, phone numbers, and addresses of all people living in a particular region. While a telephone book is certainly a ubiquitous and frequently used database, it suffers from the following:
Finding a person’s telephone number can be time-consuming, especially if the telephone book contains a large number of entries.
A telephone book is indexed only by last/first names, so finding the names of the people living at a particular address, while possible in theory, is not a practical use for this database.
From the moment the telephone book is printed, the information becomes less and less accurate as people move into or out of a region, change their telephone numbers, or move to another location within the same region.
The same drawbacks attributed to telephone books can also apply to any manual data storage system, such as patient records stored in a filing cabinet. Because of the cumbersome nature of paper databases, some of the first computer applications developed were database systems, which are computerized data storage and retrieval mechanisms. Because a database system stores data electronically rather than on paper, a database system is able to retrieve data more quickly, index data in multiple ways, and deliver up-to-the-minute information to its user community.
Early database systems managed data stored on magnetic tapes. Because there were generally far more tapes than tape readers, technicians were tasked with loading and unloading tapes as specific data was requested. Because the computers of that era had very little memory, multiple requests for the same data generally required the data to be read from the tape multiple times. While these database systems were a significant improvement over paper databases, they are a far cry from what is possible with today’s technology. (Modern database systems can manage terabytes of data spread across many fast-access disk drives, holding tens of gigabytes of that data in high-speed memory, but I’m getting a bit ahead of myself.)
This section contains some background information about pre-relational database systems. For those readers eager to dive into SQL, feel free to skip ahead a couple of pages to the next section.
Over the first several decades of computerized database systems, data was stored and represented to users in various ways. In a hierarchical database system, for example, data is represented as one or more tree structures. Figure 1.1, “Hierarchical view of account data” shows how data relating to George Blake’s and Sue Smith’s bank accounts might be represented via tree structures.
George and Sue each have their own tree containing their accounts and the transactions on those accounts. The hierarchical database system provides tools for locating a particular customer’s tree and then traversing the tree to find the desired accounts and/or transactions. Each node in the tree may have either zero or one parent and zero, one, or many children. This configuration is known as a single-parent hierarchy.
Another common approach, called the network database system, exposes sets of records and sets of links that define relationships between different records. Figure 1.2, “Network view of account data” shows how George’s and Sue’s same accounts might look in such a system.
In order to find the transactions posted to Sue’s money market account, you would need to perform the following steps:
Find the customer record for Sue Smith.
Follow the link from Sue Smith’s customer record to her list of accounts.
Traverse the chain of accounts until you find the money market account.
Follow the link from the money market record to its list of transactions.
One interesting feature of network database systems is
demonstrated by the set of
records on the far right of Figure 1.2, “Network view of account data”. Notice that each
product record (Checking, Savings, etc.)
points to a list of
that are of that product type.
Account records, therefore, can be accessed
from multiple places (both
allowing a network database to act as a multiparent
Both hierarchical and network database systems are alive and well today, although generally in the mainframe world. Additionally, hierarchical database systems have enjoyed a rebirth in the directory services realm, such as Microsoft’s Active Directory and the Red Hat Directory Server, as well as with Extensible Markup Language (XML). Beginning in the 1970s, however, a new way of representing data began to take root, one that was more rigorous yet easy to understand and implement.
In 1970, Dr. E. F. Codd of IBM’s research laboratory published a paper titled “A Relational Model of Data for Large Shared Data Banks” that proposed that data be represented as sets of tables. Rather than using pointers to navigate between related entities, redundant data is used to link records in different tables. Figure 1.3, “Relational view of account data” shows how George’s and Sue’s account information would appear in this context.
There are four tables in Figure 1.3, “Relational view of account data” representing the four
entities discussed so far:
transaction. Looking across the top of the
customer table in Figure 1.3, “Relational view of account data”, you can see three
(which contains the customer’s ID number),
fname (which contains the customer’s first
lname (which contains the
customer’s last name). Looking down the side of the
customer table, you can see two
rows, one containing George Blake’s data and the
other containing Sue Smith’s data. The number of columns that a table
may contain differs from server to server, but it is generally large
enough not to be an issue (Microsoft SQL Server, for example, allows up
to 1,024 columns per table). The number of rows that a table may contain
is more a matter of physical limits (i.e., how much disk drive space is
available) and maintainability (i.e., how large a table can get before
it becomes difficult to work with) than of database server
Each table in a relational database includes information that
uniquely identifies a row in that table (known as the primary
key), along with additional information needed to describe
the entity completely. Looking again at the
customer table, the
cust_id column holds a different number for
each customer; George Blake, for example, can be uniquely identified by
customer ID #1. No other customer will ever be assigned that identifier,
and no other information is needed to locate George Blake’s data in the
Every database server provides a mechanism for generating unique sets of numbers to use as primary key values, so you won’t need to worry about keeping track of what numbers have been assigned.
While I might have chosen to use the combination of the
lname columns as the primary key (a primary
key consisting of two or more columns is known as a compound
key), there could easily be two or more people with the same
first and last names that have accounts at the bank. Therefore, I chose
to include the
cust_id column in the
customer table specifically for use
as a primary key column.
In this example, choosing
lname as the primary key would be referred to as a natural
key, whereas the choice of
cust_id would be referred to as a
surrogate key. The decision whether to employ
natural or surrogate keys is a topic of widespread debate, but in this
particular case the choice is clear, since a person’s last name may
change (such as when a person adopts a spouse’s last name), and
primary key columns should never be allowed to change once a value has
Some of the tables also include information used to navigate to
another table; this is where the “redundant data” mentioned earlier
comes in. For example, the
table includes a column called
cust_id, which contains the unique identifier
of the customer who opened the account, along with a column called
product_cd, which contains the unique
identifier of the product to which the account will conform. These
columns are known as foreign keys, and they serve
the same purpose as the lines that connect the entities in the
hierarchical and network versions of the account information. If you are
looking at a particular account record and want to know more information
about the customer who opened the account, you would take the value of
cust_id column and use it to find
the appropriate row in the
table (this process is known, in relational database lingo, as a
join; joins are introduced in Chapter 3, Query Primer and probed deeply in Chapters 5 and 10).
It might seem wasteful to store the same data many times, but the
relational model is quite clear on what redundant data may be stored.
For example, it is proper for the
account table to include a column for the
unique identifier of the customer who opened the account, but it is not
proper to include the customer’s first and last names in the
account table as well. If a customer were to
change her name, for example, you want to make sure that there is only
one place in the database that holds the customer’s name; otherwise, the
data might be changed in one place but not another, causing the data in
the database to be unreliable. The proper place for this data is the
customer table, and only the
cust_id values should be included in other
tables. It is also not proper for a single column to contain multiple
pieces of information, such as a
column that contains both a person’s first and last names, or an
address column that contains street,
city, state, and zip code information. The process of refining a
database design to ensure that each independent piece of information is
in only one place (except for foreign keys) is known as
Getting back to the four tables in Figure 1.3, “Relational view of account data”, you may wonder how you
would use these tables to find George Blake’s transactions against his
checking account. First, you would find George Blake’s unique identifier
customer table. Then, you
would find the row in the
cust_id column contains
George’s unique identifier and whose
product_cd column matches the row in the
product table whose
name column equals “Checking.” Finally, you
would locate the rows in the
transaction table whose
account_id column matches the unique
identifier from the
This might sound complicated, but you can do it in a single command,
using the SQL language, as you will see shortly.
I introduced some new terminology in the previous sections, so maybe it’s time for some formal definitions. Table 1.1, “Terms and definitions” shows the terms we use for the remainder of the book along with their definitions.
Table 1.1. Terms and definitions
Something of interest to the database user community. Examples include customers, parts, geographic locations, etc.
An individual piece of data stored in a table.
A set of columns that together completely describe an entity or some action on an entity. Also called a record.
A set of rows, held either in memory (nonpersistent) or on permanent storage (persistent).
Another name for a nonpersistent table, generally the result of an SQL query.
One or more columns that can be used as a unique identifier for each row in a table.
Along with Codd’s definition of the relational model, he proposed a language called DSL/Alpha for manipulating the data in relational tables. Shortly after Codd’s paper was released, IBM commissioned a group to build a prototype based on Codd’s ideas. This group created a simplified version of DSL/Alpha that they called SQUARE. Refinements to SQUARE led to a language called SEQUEL, which was, finally, renamed SQL.
SQL is now entering middle age (as is this author, alas), and it has undergone a great deal of change along the way. In the mid-1980s, the American National Standards Institute (ANSI) began working on the first standard for the SQL language, which was published in 1986. Subsequent refinements led to new releases of the SQL standard in 1989, 1992, 1999, 2003, and 2006. Along with refinements to the core language, new features have been added to the SQL language to incorporate object-oriented functionality, among other things. The latest standard, SQL:2006, focuses on the integration of SQL and XML and defines a language called XQuery which is used to query data in XML documents.
SQL goes hand in hand with the relational model because the result of an SQL query is a table (also called, in this context, a result set). Thus, a new permanent table can be created in a relational database simply by storing the result set of a query. Similarly, a query can use both permanent tables and the result sets from other queries as inputs (we explore this in detail in Chapter 9, Subqueries).
One final note: SQL is not an acronym for anything (although many people will insist it stands for “Structured Query Language”). When referring to the language, it is equally acceptable to say the letters individually (i.e., S. Q. L.) or to use the word sequel.
The SQL language is divided into several distinct parts: the parts
that we explore in this book include SQL schema
statements, which are used to define the data structures
stored in the database; SQL data statements, which
are used to manipulate the data structures previously defined using SQL
schema statements; and SQL transaction statements,
which are used to begin, end, and roll back transactions (covered in Chapter 12, Transactions). For example, to create a new table in
your database, you would use the SQL schema statement
create table, whereas the process of
populating your new table with data would require the SQL data statement
To give you a taste of what these statements look like, here’s an
SQL schema statement that creates a table called
CREATE TABLE corporation (corp_id SMALLINT, name VARCHAR(30), CONSTRAINT pk_corporation PRIMARY KEY (corp_id) );
This statement creates a table with two columns,
name, with the
corp_id column identified as the primary key
for the table. We probe the finer details of this statement, such as the
different data types available with MySQL, in Chapter 2, Creating and Populating a Database. Next, here’s an SQL
data statement that inserts a row into the
corporation table for Acme Paper
INSERT INTO corporation (corp_id, name) VALUES (27, 'Acme Paper Corporation');
This statement adds a row to the
corporation table with a value of
27 for the
corp_id column and a value of
Acme Paper Corporation for the
Finally, here’s a simple
statement to retrieve the data that was just created:
WHERE corp_id = 27;+------------------------+ | name | +------------------------+ | Acme Paper Corporation | +------------------------+
All database elements created via SQL schema statements are stored
in a special set of tables called the data
dictionary. This “data about the database” is known
collectively as metadata and is explored in Chapter 15, Metadata. Just like tables that you create yourself, data
dictionary tables can be queried via a
select statement, thereby allowing you to
discover the current data structures deployed in the database at
runtime. For example, if you are asked to write a report showing the new
accounts created last month, you could either hardcode the names of the
columns in the
account table that
were known to you when you wrote the report, or query the data
dictionary to determine the current set of columns and dynamically
generate the report each time it is executed.
Most of this book is concerned with the data portion of the SQL
language, which consists of the
delete commands. SQL schema statements is
demonstrated in Chapter 2, Creating and Populating a Database,
where the sample database used throughout this book is generated. In
general, SQL schema statements do not require much discussion apart from
their syntax, whereas SQL data statements, while few in number, offer
numerous opportunities for detailed study. Therefore, while I try to
introduce you to many of the SQL schema statements, most chapters in
this book concentrate on the SQL data statements.
If you have worked with programming languages in the past, you are used to defining variables and data structures, using conditional logic (i.e., if-then-else) and looping constructs (i.e., do while ... end), and breaking your code into small, reusable pieces (i.e., objects, functions, procedures). Your code is handed to a compiler, and the executable that results does exactly (well, not always exactly) what you programmed it to do. Whether you work with Java, C#, C, Visual Basic, or some other procedural language, you are in complete control of what the program does.
A procedural language defines both the desired results and the mechanism, or process, by which the results are generated. Nonprocedural languages also define the desired results, but the process by which the results are generated is left to an external agent.
With SQL, however, you will need to give up some of the control you are used to, because SQL statements define the necessary inputs and outputs, but the manner in which a statement is executed is left to a component of your database engine known as the optimizer. The optimizer’s job is to look at your SQL statements and, taking into account how your tables are configured and what indexes are available, decide the most efficient execution path (well, not always the most efficient). Most database engines will allow you to influence the optimizer’s decisions by specifying optimizer hints, such as suggesting that a particular index be used; most SQL users, however, will never get to this level of sophistication and will leave such tweaking to their database administrator or performance expert.
With SQL, therefore, you will not be able to write complete applications. Unless you are writing a simple script to manipulate certain data, you will need to integrate SQL with your favorite programming language. Some database vendors have done this for you, such as Oracle’s PL/SQL language, MySQL’s stored procedure language, and Microsoft’s Transact-SQL language. With these languages, the SQL data statements are part of the language’s grammar, allowing you to seamlessly integrate database queries with procedural commands. If you are using a non-database-specific language such as Java, however, you will need to use a toolkit/API to execute SQL statements from your code. Some of these toolkits are provided by your database vendor, whereas others are created by third-party vendors or by open source providers. Table 1.2, “SQL integration toolkits” shows some of the available options for integrating SQL into a specific language.
Table 1.2. SQL integration toolkits
JDBC (Java Database Connectivity; JavaSoft)
Rogue Wave SourcePro DB (third-party tool to connect to Oracle, SQL Server, MySQL, Informix, DB2, Sybase, and PostgreSQL databases)
Pro*C (Oracle), MySQL C API (open source), and DB2 Call Level Interface (IBM)
If you only need to execute SQL commands interactively, every
database vendor provides at least a simple command-line tool for
submitting SQL commands to the database engine and inspecting the
results. Most vendors provide a graphical tool as well that includes one
window showing your SQL commands and another window showing the results
from your SQL commands. Since the examples in this book are executed
against a MySQL database, I use the
mysql command-line tool that is included as
part of the MySQL installation to run the examples and format the
SELECT t.txn_id, t.txn_type_cd, t.txn_date, t.amount FROM individual i INNER JOIN account a ON i.cust_id = a.cust_id INNER JOIN product p ON p.product_cd = a.product_cd INNER JOIN transaction t ON t.account_id = a.account_id WHERE i.fname = 'George' AND i.lname = 'Blake' AND p.name = 'checking account'; +--------+-------------+---------------------+--------+ | txn_id | txn_type_cd | txn_date | amount | +--------+-------------+---------------------+--------+ | 11 | DBT | 2008-01-05 00:00:00 | 100.00 | +--------+-------------+---------------------+--------+ 1 row in set (0.00 sec)
Without going into too much detail at this point, this query
identifies the row in the
table for George Blake and the row in the
product table for the “checking” product,
finds the row in the
for this individual/product combination, and returns four columns from
transaction table for all
transactions posted to this account. If you happen to know that George
Blake’s customer ID is 8 and that checking accounts are designated by
'CHK', then you can simply
find George Blake’s checking account in the
account table based on the customer ID and use
the account ID to find the appropriate transactions:
SELECT t.txn_id, t.txn_type_cd, t.txn_date, t.amount FROM account a INNER JOIN transaction t ON t.account_id = a.account_id WHERE a.cust_id = 8 AND a.product_cd = 'CHK';
I cover all of the concepts in these queries (plus a lot more) in the following chapters, but I wanted to at least show what they would look like.
The previous queries contain three different
where. Almost every query that you encounter
will include at least these three clauses, although there are several
more that can be used for more specialized purposes. The role of each of
these three clauses is demonstrated by the following:
SELECT /* one or more things */ ... FROM /* one or more places */ ... WHERE /* one or more conditions apply */ ...
Most SQL implementations treat any text between the
tags as comments.
When constructing your query, your first task is generally to
determine which table or tables will be needed and then add them to your
from clause. Next, you will need to
add conditions to your
to filter out the data from these tables that you aren’t interested in.
Finally, you will decide which columns from the different tables need to
be retrieved and add them to your
select clause. Here’s a simple example that
shows how you would find all customers with the last name
SELECT cust_id, fname FROM individual WHERE lname = 'Smith';
This query searches the
individual table for all rows whose
lname column matches the string
'Smith' and returns the
fname columns from those rows.
Along with querying your database, you will most likely be
involved with populating and modifying the data in your database. Here’s
a simple example of how you would insert a new row into the
INSERT INTO product (product_cd, name) VALUES ('CD', 'Certificate of Depysit')
UPDATE product SET name = 'Certificate of Deposit' WHERE product_cd = 'CD';
Notice that the
statement also contains a
clause, just like the
statement. This is because an
statement must identify the rows to be modified; in this case, you are
specifying that only those rows whose
product_cd column matches the string
'CD' should be modified. Since the
product_cd column is the primary key for the
product table, you should expect your
update statement to modify exactly
one row (or zero, if the value doesn’t exist in the table). Whenever you
execute an SQL data statement, you will receive feedback from the
database engine as to how many rows were affected by your statement. If
you are using an interactive tool such as the
mysql command-line tool mentioned earlier,
then you will receive feedback concerning how many rows were
Returned by your
Created by your
Modified by your
Removed by your
If you are using a procedural language with one of the toolkits
mentioned earlier, the toolkit will include a call to ask for this
information after your SQL data statement has executed. In general, it’s
a good idea to check this info to make sure your statement didn’t do
something unexpected (like when you forget to put a
where clause on your
delete statement and delete every row in the
Oracle Database from Oracle Corporation
SQL Server from Microsoft
DB2 Universal Database from IBM
Sybase Adaptive Server from Sybase
All these database servers do approximately the same thing, although some are better equipped to run very large or very-high-throughput databases. Others are better at handling objects or very large files or XML documents, and so on. Additionally, all these servers do a pretty good job of complying with the latest ANSI SQL standard. This is a good thing, and I make it a point to show you how to write SQL statements that will run on any of these platforms with little or no modification.
Along with the commercial database servers, there has been quite a
bit of activity in the open source community in the past five years with
the goal of creating a viable alternative to the commercial database
servers. Two of the most commonly used open source database servers are
PostgreSQL and MySQL. The MySQL website (http://www.mysql.com) currently claims over 10 million
installations, its server is available for free, and I have found its
server to be extremely simple to download and install. For these reasons,
I have decided that all examples for this book be run against a MySQL
(version 6.0) database, and that the
mysql command-line tool be used to format query
results. Even if you are already using another server and never plan to
use MySQL, I urge you to install the latest MySQL server, load the sample
schema and data, and experiment with the data and examples in this
However, keep in mind the following caveat:
This is not a book about MySQL’s SQL implementation.
Rather, this book is designed to teach you how to craft SQL statements that will run on MySQL with no modifications, and will run on recent releases of Oracle Database, Sybase Adaptive Server, and SQL Server with few or no modifications.
To keep the code in this book as vendor-independent as possible, I will refrain from demonstrating some of the interesting things that the MySQL SQL language implementers have decided to do that can’t be done on other database implementations. Instead, Appendix B, MySQL Extensions to the SQL Language covers some of these features for readers who are planning to continue using MySQL.
The overall goal of the next four chapters is to introduce the SQL
data statements, with a special emphasis on the three main clauses of the
select statement. Additionally, you
will see many examples that use the bank schema (introduced in the next
chapter), which will be used for all examples in the book. It is my hope
that familiarity with a single database will allow you to get to the crux
of an example without your having to stop and examine the tables being
used each time. If it becomes a bit tedious working with the same set of
tables, feel free to augment the sample database with additional tables,
or invent your own database with which to experiment.
After you have a solid grasp on the basics, the remaining chapters will drill deep into additional concepts, most of which are independent of each other. Thus, if you find yourself getting confused, you can always move ahead and come back later to revisit a chapter. When you have finished the book and worked through all of the examples, you will be well on your way to becoming a seasoned SQL practitioner.
For readers interested in learning more about relational databases, the history of computerized database systems, or the SQL language than was covered in this short introduction, here are a few resources worth checking out:
C.J. Date’s Database in Depth: Relational Theory for Practitioners (O’Reilly)
C.J. Date’s An Introduction to Database Systems, Eighth Edition (Addison-Wesley)
C.J. Date’s The Database Relational Model: A Retrospective Review and Analysis: A Historical Account and Assessment of E. F. Codd’s Contribution to the Field of Database Technology (Addison-Wesley)
If you enjoyed this excerpt, buy a copy of Learning SQL, Second Edition.
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